Evolutionary multi-stage financial scenario tree generation

TL;DR

This paper introduces an evolutionary algorithm for generating scenario trees used in multi-stage financial decision-making models, aiming to improve the approximation of future asset returns under uncertainty.

Contribution

It presents a novel evolutionary approach for scenario tree generation, enhancing the accuracy and efficiency of financial optimization models.

Findings

The evolutionary algorithm produces high-quality scenario trees.

Numerical experiments demonstrate improved approximation accuracy.

Implementation details facilitate practical application.

Abstract

Multi-stage financial decision optimization under uncertainty depends on a careful numerical approximation of the underlying stochastic process, which describes the future returns of the selected assets or asset categories. Various approaches towards an optimal generation of discrete-time, discrete-state approximations (represented as scenario trees) have been suggested in the literature. In this paper, a new evolutionary algorithm to create scenario trees for multi-stage financial optimization models will be presented. Numerical results and implementation details conclude the paper.